并发环境下为什么使用ConcurrentHashMap
1. HashMap在高并发的环境下,执行put操作会导致HashMap的Entry链表形成环形数据结构,从而导致Entry的next节点始终不为空,因此产生死循环获取Entry
2. HashTable虽然是线程安全的,但是效率低下,当一个线程访问HashTable的同步方法时,其他线程如果也访问HashTable的同步方法,那么会进入阻塞或者轮训状态。
3. 在jdk1.6中ConcurrentHashMap使用锁分段技术提高并发访问效率。首先将数据分成一段一段地存储,然后给每一段数据配一个锁,当一个线程占用锁访问其中一段数据时,其他段的数据也能被其他线程访问。然而在jdk1.8中的实现已经抛弃了Segment分段锁机制,利用CAS+Synchronized来保证并发更新的安全,底层依然采用数组+链表+红黑树的存储结构。
JDK1.6分析
ConcurrentHashMap采用 分段锁的机制,实现并发的更新操作,底层由Segment数组和HashEntry数组组成。Segment继承ReentrantLock用来充当锁的角色,每个 Segment 对象守护每个散列映射表的若干个桶。HashEntry 用来封装映射表的键 / 值对;每个桶是由若干个 HashEntry 对象链接起来的链表。一个 ConcurrentHashMap 实例中包含由若干个 Segment 对象组成的数组,下面我们通过一个图来演示一下 ConcurrentHashMap 的结构:
JDK1.8分析
改进一:取消segments字段,直接采用transient volatile HashEntry<K,V> table
保存数据,采用table数组元素作为锁,从而实现了对每一行数据进行加锁,进一步减少并发冲突的概率。
改进二:将原先table数组+单向链表的数据结构,变更为table数组+单向链表+红黑树的结构。对于hash表来说,最核心的能力在于将key hash之后能均匀的分布在数组中。如果hash之后散列的很均匀,那么table数组中的每个队列长度主要为0或者1。但实际情况并非总是如此理想,虽然ConcurrentHashMap类默认的加载因子为0.75,但是在数据量过大或者运气不佳的情况下,还是会存在一些队列长度过长的情况,如果还是采用单向列表方式,那么查询某个节点的时间复杂度为O(n);因此,对于个数超过8(默认值)的列表,jdk1.8中采用了红黑树的结构,那么查询的时间复杂度可以降低到O(logN),可以改进性能。
ConcurrentHashMap的重要属性
/** * races. Updated via CAS. * 记录容器的容量大小,通过CAS更新 */ private static final long BASECOUNT;/** * 这个sizeCtl是volatile的,那么他是线程可见的,一个思考:它是所有修改都在CAS中进行,但是sizeCtl为什么不设计成LongAdder(jdk8出现的)类型呢? * 或者设计成AtomicLong(在高并发的情况下比LongAdder低效),这样就能减少自己操作CAS了。 * * 默认为0,用来控制table的初始化和扩容操作,具体应用在后续会体现出来。 * -1 代表table正在初始化 * -N 表示有N-1个线程正在进行扩容操作 * 其余情况: *1、如果table未初始化,表示table需要初始化的大小。 *2、如果table初始化完成,表示table的容量,默认是table大小的0.75 倍,居然用这个公式算0.75(n - (n >>> 2))。 **/private static final long SIZECTL;/** * 自旋锁 (锁定通过 CAS) 在调整大小和/或创建 CounterCells 时使用。 在CounterCell类更新value中会使用,功能类似显示锁和内置锁,性能更好 * 在Striped64类也有应用 */ private static final long CELLSBUSY;
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Node:保存key,value及key的hash值的数据结构。其中value和next都用volatile修饰,保证并发的可见性。
static class Node<K,V> implements Map.Entry<K,V> { final int hash; final K key; volatile V val; volatile Node<K,V> next; Node(int hash, K key, V val, Node<K,V> next) { this.hash = hash; this.key = key; this.val = val; this.next = next; } }
ForwardingNode:一个特殊的Node节点,hash值为-1,其中存储nextTable的引用。
static final class ForwardingNode<K,V> extends Node<K,V> { final Node<K,V>[] nextTable; ForwardingNode(Node<K,V>[] tab) { super(MOVED, null, null, null); this.nextTable = tab; } }
ConcurrentHashMap的构造函数
public ConcurrentHashMap(){} /** *initialCapacity 初始化容量 **/ public ConcurrentHashMap(int initialCapacity) {} /** * *创建与给定map具有相同映射的新map **/ public ConcurrentHashMap(Map<? extends K, ? extends V> m){} /** *initialCapacity 初始容量 *loadFactor 负载因子,当容量达到initialCapacity*loadFactor时,执行扩容 *concurrencyLevel 预估的并发更新线程数 **/ public ConcurrentHashMap(int initialCapacity, float loadFactor) {} /** *initialCapacity 初始容量 *loadFactor 负载因子 *concurrencyLevel 预估的并发更新线程数 **/ public ConcurrentHashMap(int initialCapacity, float loadFactor, int concurrencyLevel) {}
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接下来具体看看第四个构造函数的具体实现:
public ConcurrentHashMap(int initialCapacity, float loadFactor, int concurrencyLevel) { if (!(loadFactor > 0.0f) || initialCapacity < 0 || concurrencyLevel <= 0) throw new IllegalArgumentException(); if (initialCapacity < concurrencyLevel) initialCapacity = concurrencyLevel; long size = (long)(1.0 + (long)initialCapacity / loadFactor); int cap = (size >= (long)MAXIMUM_CAPACITY) ? MAXIMUM_CAPACITY : tableSizeFor((int)size); this.sizeCtl = cap; } /** *返回给定所需容量,table的大小总是2的幂次方 **/ private static final int tableSizeFor(int c) { int n = c - 1; n |= n >>> 1; n |= n >>> 2; n |= n >>> 4; n |= n >>> 8; n |= n >>> 16; return (n < 0) ? 1 : (n >= MAXIMUM_CAPACITY) ? MAXIMUM_CAPACITY : n + 1; }
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ConcurrentHashMap在构造函数中只会初始化sizeCtl值,并不会直接初始化table,而是延缓到第一次put操作
put()方法的实现
public V put(K key, V value) { return putVal(key, value, false); } final V putVal(K key, V value, boolean onlyIfAbsent) { if (key == null || value == null) throw new NullPointerException(); int hash = spread(key.hashCode()); int binCount = 0; for (Node<K,V>[] tab = table;;) { Node<K,V> f; int n, i, fh; if (tab == null || (n = tab.length) == 0) tab = initTable(); else if ((f = tabAt(tab, i = (n - 1) & hash)) == null) { if (casTabAt(tab, i, null, new Node<K,V>(hash, key, value, null))) break; } else if ((fh = f.hash) == MOVED) tab = helpTransfer(tab, f); else { V oldVal = null; synchronized (f) { if (tabAt(tab, i) == f) { if (fh >= 0) { binCount = 1; for (Node<K,V> e = f;; ++binCount) { K ek; if (e.hash == hash && ((ek = e.key) == key || (ek != null && key.equals(ek)))) { oldVal = e.val; if (!onlyIfAbsent) e.val = value; break; } Node<K,V> pred = e; if ((e = e.next) == null) { pred.next = new Node<K,V>(hash, key, value, null); break; } } } else if (f instanceof TreeBin) { Node<K,V> p; binCount = 2; if ((p = ((TreeBin<K,V>)f).putTreeVal(hash, key, value)) != null) { oldVal = p.val; if (!onlyIfAbsent) p.val = value; } } } } if (binCount != 0) { if (binCount >= TREEIFY_THRESHOLD) treeifyBin(tab, i); if (oldVal != null) return oldVal; break; } } } addCount(1L, binCount); return null;}@SuppressWarnings("unchecked")static final <K,V> Node<K,V> tabAt(Node<K,V>[] tab, int i) { return (Node<K,V>)U.getObjectVolatile(tab, ((long)i << ASHIFT) + ABASE);}static final <K,V> boolean casTabAt(Node<K,V>[] tab, int i, Node<K,V> c, Node<K,V> v) { return U.compareAndSwapObject(tab, ((long)i << ASHIFT) + ABASE, c, v);}static final <K,V> void setTabAt(Node<K,V>[] tab, int i, Node<K,V> v) { U.putObjectVolatile(tab, ((long)i << ASHIFT) + ABASE, v);}
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我们还是继续一步步看代码,看inputVal的注释a,这个方法helpTransfer,如果线程进入到这边说明已经有其他线程正在做扩容操作,这个是一个辅助方法
/** * Helps transfer if a resize is in progress. */final Node<K,V>[] helpTransfer(Node<K,V>[] tab, Node<K,V> f) { Node<K,V>[] nextTab; int sc; if (tab != null && (f instanceof ForwardingNode) && (nextTab = ((ForwardingNode<K,V>)f).nextTable) != null) { int rs = resizeStamp(tab.length); while (nextTab == nextTable && table == tab && (sc = sizeCtl) < 0) { if ((sc >>> RESIZE_STAMP_SHIFT) != rs || sc == rs + 1 || sc == rs + MAX_RESIZERS || transferIndex <= 0) break; if (U.compareAndSwapInt(this, SIZECTL, sc, sc + 1)) { transfer(tab, nextTab); break; } } return nextTab; } return table;}
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当我们的putVal执行到addCount的时候
private final void addCount(long x, int check) { CounterCell[] as; long b, s; if ((as = counterCells) != null || !U.compareAndSwapLong(this, BASECOUNT, b = baseCount, s = b + x)) { CounterCell a; long v; int m; boolean uncontended = true; if (as == null || (m = as.length - 1) < 0 || (a = as[ThreadLocalRandom.getProbe() & m]) == null || !(uncontended = U.compareAndSwapLong(a, CELLVALUE, v = a.value, v + x))) { fullAddCount(x, uncontended); return; } if (check <= 1) return; s = sumCount(); } if (check >= 0) { Node<K,V>[] tab, nt; int n, sc; while (s >= (long)(sc = sizeCtl) && (tab = table) != null && (n = tab.length) < MAXIMUM_CAPACITY) { int rs = resizeStamp(n); if (sc < 0) { if ((sc >>> RESIZE_STAMP_SHIFT) != rs || sc == rs + 1 || sc == rs + MAX_RESIZERS || (nt = nextTable) == null || transferIndex <= 0) break; if (U.compareAndSwapInt(this, SIZECTL, sc, sc + 1)) transfer(tab, nt); } else if (U.compareAndSwapInt(this, SIZECTL, sc, (rs << RESIZE_STAMP_SHIFT) + 2)) transfer(tab, null); s = sumCount(); } }}
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看上面注释1,每次都会对baseCount 加1,如果并发竞争太大,那么可能导致U.compareAndSwapLong(this, BASECOUNT, b = baseCount, s = b + x) 失败,那么为了提高高并发的时候baseCount可见性失败的问题,又避免一直重试,这样性能会有很大的影响,那么在jdk8的时候是有引入一个类Striped64,其中LongAdder和DoubleAdder就是对这个类的实现。这两个方法都是为解决高并发场景而生的,是AtomicLong的加强版,AtomicLong在高并发场景性能会比LongAdder差。但是LongAdder的空间复杂度会高点。
我们每次进来都对baseCount进行加1当达到一定的容量时,就需要对table进行扩容。扩容方法就是transfer,这个方法稍微复杂一点,大部分的代码我都做了注释
private final void transfer(Node<K,V>[] tab, Node<K,V>[] nextTab) { int n = tab.length, stride; if ((stride = (NCPU > 1) ? (n >>> 3) / NCPU : n) < MIN_TRANSFER_STRIDE) stride = MIN_TRANSFER_STRIDE; if (nextTab == null) { try { @SuppressWarnings("unchecked") Node<K,V>[] nt = (Node<K,V>[])new Node<?,?>[n << 1]; nextTab = nt; } catch (Throwable ex) { sizeCtl = Integer.MAX_VALUE; return; } nextTable = nextTab; transferIndex = n; } int nextn = nextTab.length; ForwardingNode<K,V> fwd = new ForwardingNode<K,V>(nextTab); boolean advance = true; boolean finishing = false; for (int i = 0, bound = 0;;) { Node<K,V> f; int fh; while (advance) { int nextIndex, nextBound; if (--i >= bound || finishing) advance = false; else if ((nextIndex = transferIndex) <= 0) { i = -1; advance = false; } else if (U.compareAndSwapInt (this, TRANSFERINDEX, nextIndex, nextBound = (nextIndex > stride ? nextIndex - stride : 0))) { bound = nextBound; i = nextIndex - 1; advance = false; } } if (i < 0 || i >= n || i + n >= nextn) { int sc; if (finishing) { nextTable = null; table = nextTab; sizeCtl = (n << 1) - (n >>> 1); return; } if (U.compareAndSwapInt(this, SIZECTL, sc = sizeCtl, sc - 1)) { if ((sc - 2) != resizeStamp(n) << RESIZE_STAMP_SHIFT) return; finishing = advance = true; i = n; } } else if ((f = tabAt(tab, i)) == null) advance = casTabAt(tab, i, null, fwd); else if ((fh = f.hash) == MOVED) advance = true; else { synchronized (f) { if (tabAt(tab, i) == f) { Node<K,V> ln, hn; if (fh >= 0) { int runBit = fh & n; Node<K,V> lastRun = f; for (Node<K,V> p = f.next; p != null; p = p.next) { int b = p.hash & n; if (b != runBit) { runBit = b; lastRun = p; } } if (runBit == 0) { ln = lastRun; hn = null; } else { hn = lastRun; ln = null; } for (Node<K,V> p = f; p != lastRun; p = p.next) { int ph = p.hash; K pk = p.key; V pv = p.val; if ((ph & n) == 0) ln = new Node<K,V>(ph, pk, pv, ln); else hn = new Node<K,V>(ph, pk, pv, hn); } setTabAt(nextTab, i, ln); setTabAt(nextTab, i + n, hn); setTabAt(tab, i, fwd); advance = true; } else if (f instanceof TreeBin) { TreeBin<K,V> t = (TreeBin<K,V>)f; TreeNode<K,V> lo = null, loTail = null; TreeNode<K,V> hi = null, hiTail = null; int lc = 0, hc = 0; for (Node<K,V> e = t.first; e != null; e = e.next) { int h = e.hash; TreeNode<K,V> p = new TreeNode<K,V> (h, e.key, e.val, null, null); if ((h & n) == 0) { if ((p.prev = loTail) == null) lo = p; else loTail.next = p; loTail = p; ++lc; } else { if ((p.prev = hiTail) == null) hi = p; else hiTail.next = p; hiTail = p; ++hc; } } ln = (lc <= UNTREEIFY_THRESHOLD) ? untreeify(lo) : (hc != 0) ? new TreeBin<K,V>(lo) : t; hn = (hc <= UNTREEIFY_THRESHOLD) ? untreeify(hi) : (lc != 0) ? new TreeBin<K,V>(hi) : t; setTabAt(nextTab, i, ln); setTabAt(nextTab, i + n, hn); setTabAt(tab, i, fwd); advance = true; } } } } }}
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值得细细品味的是,transfer的for循环是倒叙的,说明对table的遍历是从table.length-1开始到0的。我觉得这段代码写得太牛逼了,特别是
if (U.compareAndSwapInt(this, SIZECTL, sc = sizeCtl, sc - 1)) { if ((sc - 2) != resizeStamp(n) << RESIZE_STAMP_SHIFT) return; finishing = advance = true; i = n; }
注意:如果链表结构中元素超过TREEIFY_THRESHOLD阈值,默认为8个,则把链表转化为红黑树,提高遍历查询效率.接下来我们看看如何构造树结构,代码如下:
private final void treeifyBin(Node<K,V>[] tab, int index) { Node<K,V> b; int n, sc; if (tab != null) { if ((n = tab.length) < MIN_TREEIFY_CAPACITY) tryPresize(n << 1); else if ((b = tabAt(tab, index)) != null && b.hash >= 0) { synchronized (b) { if (tabAt(tab, index) == b) { TreeNode<K,V> hd = null, tl = null; for (Node<K,V> e = b; e != null; e = e.next) { TreeNode<K,V> p = new TreeNode<K,V>(e.hash, e.key, e.val, null, null); if ((p.prev = tl) == null) hd = p; else tl.next = p; tl = p; } setTabAt(tab, index, new TreeBin<K,V>(hd)); } } } }}
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可以看出,生成树节点的代码块是同步的,进入同步代码块之后,再次验证table中index位置元素是否被修改过。
1、根据table中index位置Node链表,重新生成一个hd为头结点的TreeNode链表。
2、根据hd头结点,生成TreeBin树结构,并把树结构的root节点写到table的index位置的内存中,具体实现如下:
TreeBin(TreeNode<K,V> b) { super(TREEBIN, null, null, null); this.first = b; TreeNode<K,V> r = null; for (TreeNode<K,V> x = b, next; x != null; x = next) { next = (TreeNode<K,V>)x.next; x.left = x.right = null; if (r == null) { x.parent = null; x.red = false; r = x; } else { K k = x.key; int h = x.hash; Class<?> kc = null; for (TreeNode<K,V> p = r;;) { int dir, ph; K pk = p.key; if ((ph = p.hash) > h) dir = -1; else if (ph < h) dir = 1; else if ((kc == null && (kc = comparableClassFor(k)) == null) || (dir = compareComparables(kc, k, pk)) == 0) dir = tieBreakOrder(k, pk); TreeNode<K,V> xp = p; if ((p = (dir <= 0) ? p.left : p.right) == null) { x.parent = xp; if (dir <= 0) xp.left = x; else xp.right = x; r = balanceInsertion(r, x); break; } } } } this.root = r; assert checkInvariants(root);}
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get()方法
public V get(Object key) { Node<K,V>[] tab; Node<K,V> e, p; int n, eh; K ek; int h = spread(key.hashCode()); if ((tab = table) != null && (n = tab.length) > 0 && (e = tabAt(tab, (n - 1) & h)) != null) { if ((eh = e.hash) == h) { if ((ek = e.key) == key || (ek != null && key.equals(ek))) return e.val; } else if (eh < 0) return (p = e.find(h, key)) != null ? p.val : null; while ((e = e.next) != null) { if (e.hash == h && ((ek = e.key) == key || (ek != null && key.equals(ek)))) return e.val; } } return null;}
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这个get请求,我们需要cas来保证变量的原子性。如果tab[i]正被锁住,那么CAS就会失败,失败之后就会不断的重试。这也保证了get在高并发情况下不会出错。
我们来分析下到底有多少种情况会导致get在并发的情况下可能取不到值。1、一个线程在get的时候,另一个线程在对同一个key的node进行remove操作;2、一个线程在get的时候,另一个线程正则重排table。可能导致旧table取不到值。
那么本质是,我在get的时候,有其他线程在对同一桶的链表或树进行修改。那么get是怎么保证同步性的呢?我们看到e = tabAt(tab, (n - 1) & h)) != null,在看下tablAt到底是干嘛的:
static final <K,V> Node<K,V> tabAt(Node<K,V>[] tab, int i) { return (Node<K,V>)U.getObjectVolatile(tab, ((long)i << ASHIFT) + ABASE);}
它是对tab[i]进行原子性的读取,因为我们知道putVal等对table的桶操作是有加锁的,那么一般情况下我们对桶的读也是要加锁的,但是我们这边为什么不需要加锁呢?因为我们用了Unsafe的getObjectVolatile,因为table是volatile类型,所以对tab[i]的原子请求也是可见的。因为如果同步正确的情况下,根据happens-before原则,对volatile域的写入操作happens-before于每一个后续对同一域的读操作。所以不管其他线程对table链表或树的修改,都对get读取可见。
参考
深入浅出ConcurrentHashMap(1.8) 作者 占小狼
探索jdk8之ConcurrentHashMap 的实现机制 作者 淮左
Java并发编程总结4——ConcurrentHashMap在jdk1.8中的改进 作者 everSeeker
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